Pratibha Shashikant Gayke

EDUCATION

I have completed B.E(I.T), M.Tech (CSE) & PhD (CSE)

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering, Human-Computer Interaction

Scopus Publications

Face and liveness detection with criminal identification using machine learning and image processing techniques for security system
Pratibha Shinde, Ajay R. Raundale
Iaes International Journal of Artificial Intelligence, 2024
<p>In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.</p>
Secure Face and Liveness Detection with Criminal Identification for Security Systems
Pratibha Shinde, Ajay Raundale
International Journal on Recent and Innovation Trends in Computing and Communication, 2023
The advancement of computer vision, machine learning, and image processing techniques has opened new avenues for enhancing security systems. In this research work focuses on developing a robust and secure framework for face and liveness detection with criminal identification, specifically designed for security systems. Machine learning algorithms and image processing techniques are employed for accurate face detection and liveness verification. Advanced facial recognition methods are utilized for criminal identification. The framework incorporates ML technology to ensure data integrity and identification techniques for security system. Experimental evaluations demonstrate the system's effectiveness in detecting faces, verifying liveness, and identifying potential criminals. The proposed framework has the potential to enhance security systems, providing reliable and secure face and liveness detection for improved safety and security.The accuracy of the algorithm is 94.30 percent. The accuracy of the model is satisfactory even after the results are acquired by combining our rules inwritten by humans with conventional machine learning classification algorithms. Still, there is scope for improving and accurately classifying the attack precisely.

RECENT SCHOLAR PUBLICATIONS

Secure Data Access using Steganography and Image Based Password
PS Gayke, S Thorat, G Nagarkar, P Kusalkar, P Waditake
2022.0
Efficient flash translation layer for flash memory
S Pratibha, M Suvarna
International Journal of Scientific and Research Publications 3 (4), 1-6 , 2013
2013.0
Citations: 9
Meta Paged Flash Translation Layer
S Pratibha

MOST CITED SCHOLAR PUBLICATIONS

Efficient flash translation layer for flash memory
S Pratibha, M Suvarna
International Journal of Scientific and Research Publications 3 (4), 1-6 , 2013
2013.0
Citations: 9
Secure Data Access using Steganography and Image Based Password
PS Gayke, S Thorat, G Nagarkar, P Kusalkar, P Waditake
2022.0
Meta Paged Flash Translation Layer
S Pratibha